Mean Magnitude Of Relative Error
LITERATURE SURVEY AND ANALYSIS
Estimating the size, development time, and cost of software projects has largely been an intuitive process in which most estimators attempt to guess the number of modules, and the number of statements per module to arrive at a total statement count in the past era. Then, using some empirically determined cost per statement relationships, they arrive at a total cost for the software development project. Hence the traditional approach is essentially static. While this approach has been relatively effective for small projects of say less than 6 months duration and less than 10 man-years (MY) of effort, it starts to break down with larger projects and becomes totally ineffective for large projects.
Many different
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al, used 15 projects from business applications and compared four models: SLIM [3], COCOMO [2], Estimacs [3], and Function Points (FP) [5]. He reported an estimation error in terms of the Mean Magnitude of Relative Error (MMRE) ranging from 85% to 772%.
Conte et al. used six dam sets from widely differing environments and reported an MMRE variation between 70% and 90% for their three tested models: SLIM [3], COCOMO [2], and Jensen’s model. As a result of their investigation, they proposed a new model COPMO calibrated separately to the six data sets. This new model yielded a MMRE of 21%.
1993, the new version of COCOMO was introduced called COCOMO 2.0 which emerged in 1994 [18]. 1994, Rajiv D Banker and Hsihui Chang and Chris F Kemmerer, found it useful for cost estimation and productivity evaluation purposes’ to think of software development as an economic production process [19]. 1996, Sophie Cockroft, obtained an accurate size estimations from the early system specifications [20]. 1997, Existing models were reviewed and more focus was on accuracy. 1998, Chatzoglou created a new model called MARCS to give predictions of the resources (time, effort, cost, and people) [21]. 1999, J. J. Dolado, made a research about the estimation using the technique of Genetic Programming (GP) for exploring possible cost functions
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